Proposed strategies for mitigating the impact of high food prices on nutrition and health in Latin America and the Caribbean.
Bibliographic record
Abstract
Abstract High food prices are expected to have detrimental impacts on the dietary intake of vulnerable populations around the world, exacerbating malnutrition and poor health. Prior to the onset of the price rises, 53 million individuals in Latin America already lacked sufficient daily energy intake, and the rates of anaemia and high child stunting suggest widespread vitamin and mineral deficiencies. Where households cope with high prices by eliminating more expensive, nutrient-dense foods from their diets, the prevalence of micronutrient deficiencies will increase, especially among those with the highest nutrient needs: pregnant and lactating women, young children and the chronically ill. Unaddressed, food price increases will stunt the growth and development of a generation. This paper reviews market-based and non-market-based options for augmenting the emergency nutrition safety net in Latin American and Caribbean nations. Because the region has the unique advantage of numerous established conditional cash transfer programmes enabled by political stability and well-functioning market economies, much focus is given to their strengths, weaknesses and potential to mitigate the effects of high food prices. Yet, as these programmes sometimes fail to target the urban poor or reach marginalized rural communities that lack access to infrastructure and markets, food-based interventions remain indispensable for restoring micronutrient and health status across the region. Contextual factors, including the specific nutrient deficiencies of concern and the condition of and access to infrastructure and markets, should inform the combination of interventions selected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".